Overview of machine learning and its applications. Loss functions, numerical optimization and back-propagation. Fundamentals of feedforward neural networks. Modern architectures and techniques for training deep networks. Convolutional neural networks: basics, visualization, and techniques for efficient spatial localization in images. Recurrent neural networks and their variants. Applications of recurrent neural networks in language and image understanding, and image captioning. Recent advances in generative models learning, generative adversarial networks and variational auto encoders. Unsupervised and self-supervised representation learning. Deep reinforcement learning.
İlk dosyayı sen ekleyebilirsin — notlar, geçmiş finaller, çözümler, cheat-sheet, ne varsa. Drive linki / PDF / ZIP / fotoğraf, hepsi olur.
Şu an: mail at, ben düzenleyip yayına alayım. Form/upload UX yakında geliyor (Kimya tasarlıyor).
| Dönem | Course CPA | |
|---|---|---|
| 2025-2026 Fall | 3.59 | 1 sec · 40 öğr |
| 2024-2025 Fall | 3.42 | 1 sec · 40 öğr |
| 2023-2024 Fall | 3.59 | 1 sec · 41 öğr |
| 2022-2023 Fall | 3.16 | 1 sec · 27 öğr |
| 2021-2022 Fall | 3.48 | 1 sec · 17 öğr |
| 2020-2021 Spring | 3.40 | 1 sec · 39 öğr |
| 2019-2020 Spring | 3.55 | 1 sec · 39 öğr |
| 2018-2019 Spring | 3.56 | 1 sec · 35 öğr |
| 2016-2017 Spring | 3.62 | 1 sec · 27 öğr |
Aggregate course GPA — Bilkent STARS'tan public data. Hoca-bazlı per-section detayı için STARS evaluation report →. Öğrenci anket cevapları KVKK kapsamında defter'de tutulmaz.
There is no final exam for this course, however, any one of the following will directly result in an F grade: (1) not submitting a project or homework (including report), (2) not preparing/presenting a survey on the pre-scheduled date, (3) being absent in the midterm, (4) being absent in a project presentation.
Introduction, course structure, overview of machine learning and deep learning. Hallmarks of deep learning. Linear classifiers. Optimization. Stochastic gradient descent and contemporary variants, back-propagation. Feedforward networks and training. Activation functions, initialization, regularization, batch normalization, model selection, ensembles. Feedforward networks and training; Activation functions, initialization, regularization, batch normalization, model selection, ensembles Convolutional neural networks. Fundamentals, architectures, pooling, visualization. Convolutional neural networks. Fundamentals, architectures, pooling, visualization. Deep learning for spatial localization. Transposed convolution, efficient pooling, object detection, semantic segmentation. Recurrent neural networks. Long-short term memory (LSTM). Language models, machine translation, image captioning, video processing, visual question answering, video processing, learning from descriptions, attention. Recurrent neural networks. Long-short term memory (LSTM). Language models, machine translation, image captioning, video processing, visual question answering, video processing, learning from descriptions, attention. Recurrent neural networks. Long-short term memory (LSTM). Language models, machine translation, image captioning, video processing, visual question answering, video processing, learning from descriptions, attention. Deep generative models. Auto-encoders, variational auto-encoders, generative adversarial networks, auto-regressive models, generative image models, unsupervised and self-supervised representation learning. Deep generative models. Auto-encoders, variational auto-encoders, generative adversarial networks, auto-regressive models, generative image models, unsupervised and self-supervised representation learning. Deep reinforcement learning. Policy gradient methods, Q-Learning. Deep reinforcement learning. Project presentations. ECTS - Workload Table: Activities Number Hours Workload Homework 1 15 15 Preparation for Midterm exam 1 15 15 Presentation (including preparation) 1 10 10 Project (including preparation and presentation if applicable) 1 20 20 Report (including preparation and presentation if applicable) 1 25 25 Course hours 14 3 42 Individual or group work 14 2 28 Midterm exam 1 2 2 Total Workload: 157 Total Workload / 30: 157 / 30 5.23 ECTS Credits of the Course: 5 Type of Course: Lecture - Seminar (where students are presenters) Teaching Methods: Lecture - Presentations